中国物理B ›› 2021, Vol. 30 ›› Issue (12): 123301-123301.doi: 10.1088/1674-1056/ac3069

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Line positions, intensities, and Einstein A coefficients for 3-0 band of 12C16O: A spectroscopy learning method

Zhi-Xiang Fan(范志祥)1, Zhi-Zhang Ni(倪志樟)1, Jie-Jie He(贺洁洁)1, Yi-Fan Wang(王一凡)1, Qun-Chao Fan(樊群超)1,†, Jia Fu(付佳)1,‡, Yong-Gen Xu(徐勇根)1, Hui-Dong Li(李会东)1, Jie Ma(马杰)2, and Feng Xie(谢锋)3   

  1. 1 School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China;
    2 State Key Laboratory of Quantum Optics and Quantum Optics Devices, Laser Spectroscopy Laboratory, College of Physics and Electronics Engineering, Shanxi University, Taiyuan 030006, China;
    3 Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China
  • 收稿日期:2021-08-31 修回日期:2021-10-02 接受日期:2021-10-18 出版日期:2021-11-15 发布日期:2021-11-25
  • 通讯作者: Qun-Chao Fan, Jia Fu E-mail:fanqunchao@mail.xhu.edu.cn;fujiayouxiang@126.com
  • 基金资助:
    Project supported by the Open Research Fund of Computational Physics Key Laboratory of Sichuan Province, Yibin University (Grant No. YBXYJSWL-ZD-2020-006), the Funds for Sichuan Distinguished Scientists of China (Grant Nos. 2019JDJQ0050 and 2019JDJQ0051), the National Natural Science Foundation of China (Grant Nos. 61722507 and 11904295), the National Undergraduate Innovation and Entrepreneurship Training Program of China (Grant No. S202110650046), the State Key Laboratory Open Fund of Quantum Optics and Quantum Optics Devices, Laser Spectroscopy Laboratory (Grant No. KF201811), and the Open Research Fund Program of the Collaborative Innovation Center of Extreme Optics (Grant No. KF2020003).

Line positions, intensities, and Einstein A coefficients for 3-0 band of 12C16O: A spectroscopy learning method

Zhi-Xiang Fan(范志祥)1, Zhi-Zhang Ni(倪志樟)1, Jie-Jie He(贺洁洁)1, Yi-Fan Wang(王一凡)1, Qun-Chao Fan(樊群超)1,†, Jia Fu(付佳)1,‡, Yong-Gen Xu(徐勇根)1, Hui-Dong Li(李会东)1, Jie Ma(马杰)2, and Feng Xie(谢锋)3   

  1. 1 School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China;
    2 State Key Laboratory of Quantum Optics and Quantum Optics Devices, Laser Spectroscopy Laboratory, College of Physics and Electronics Engineering, Shanxi University, Taiyuan 030006, China;
    3 Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China
  • Received:2021-08-31 Revised:2021-10-02 Accepted:2021-10-18 Online:2021-11-15 Published:2021-11-25
  • Contact: Qun-Chao Fan, Jia Fu E-mail:fanqunchao@mail.xhu.edu.cn;fujiayouxiang@126.com
  • Supported by:
    Project supported by the Open Research Fund of Computational Physics Key Laboratory of Sichuan Province, Yibin University (Grant No. YBXYJSWL-ZD-2020-006), the Funds for Sichuan Distinguished Scientists of China (Grant Nos. 2019JDJQ0050 and 2019JDJQ0051), the National Natural Science Foundation of China (Grant Nos. 61722507 and 11904295), the National Undergraduate Innovation and Entrepreneurship Training Program of China (Grant No. S202110650046), the State Key Laboratory Open Fund of Quantum Optics and Quantum Optics Devices, Laser Spectroscopy Laboratory (Grant No. KF201811), and the Open Research Fund Program of the Collaborative Innovation Center of Extreme Optics (Grant No. KF2020003).

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摘要： Based on the model- and data-driven strategy, a spectroscopy learning method that can extract the novel and hidden information from the line list databases has been applied to the R branch emission spectra of 3-0 band of the ground electronic state of 12C16O. The labeled line lists such as line intensities and Einstein A coefficients quoted in HITRAN2020 are collected to enhance the dataset. The quantified spectroscopy-learned spectroscopic constants is beneficial for improving the extrapolative accuracy beyond the measurements. Explicit comparisons are made for line positions, line intensities, Einstein A coefficients, which demonstrate that the model- and data-driven spectroscopy learning approach is a promising and an easy-to-implement strategy.

关键词: carbon monoxide, line lists, a model- and data-driven strategy, spectroscopy learning

Abstract: Based on the model- and data-driven strategy, a spectroscopy learning method that can extract the novel and hidden information from the line list databases has been applied to the R branch emission spectra of 3-0 band of the ground electronic state of 12C16O. The labeled line lists such as line intensities and Einstein A coefficients quoted in HITRAN2020 are collected to enhance the dataset. The quantified spectroscopy-learned spectroscopic constants is beneficial for improving the extrapolative accuracy beyond the measurements. Explicit comparisons are made for line positions, line intensities, Einstein A coefficients, which demonstrate that the model- and data-driven spectroscopy learning approach is a promising and an easy-to-implement strategy.

Key words: carbon monoxide, line lists, a model- and data-driven strategy, spectroscopy learning

中图分类号:  (Vibration-rotation analysis)

  • 33.20.Vq
33.15.Mt (Rotation, vibration, and vibration-rotation constants) 33.20.Sn (Rotational analysis) 33.20.-t (Molecular spectra)